Dependence of Chemical Abundance on the Cosmic-Ray Ionization Rate in IC 348
Bibliographic record
Abstract
Abstract Ions (e.g., H 3 + , H 2 O + ) have been used extensively to quantify the cosmic-ray ionization rate (CRIR) in diffuse sightlines. However, measurements of the CRIR in low-to-intermediate density gas environments are rare, especially when background stars are absent. In this work, we combine molecular line observations of CO, OH, CH, and HCO + in the star-forming cloud IC 348, and chemical models to constrain the value of the CRIR and study the response of the distribution of chemical abundances. The cloud boundary is found to have an A V of approximately 4 mag. From the interior to the exterior of the cloud, the observed 13 CO line intensities drop by an order of magnitude. The calculated average abundance of 12 CO (assuming 12 C/ 13 C=65) is (1.2 ± 0.9) × 10 −4 , which decreases by a factor of 6 from the interior to the outside regions. The average abundance of CH (3.3 ± 0.7 × 10 −8 ) is in good agreement with previous findings in diffuse and translucent clouds ( A V < 5 mag). However, we did not find a decline in CH abundance in regions of high extinction ( A V ≃ 8 mag) as previously reported in Taurus. By comparing the observed molecular abundances and chemical models, we find a decreasing trend of the CRIR as A V increases. The inferred CRIR of ζ cr = (4.7 ± 1.5) × 10 −16 s −1 at low A V is consistent with H 3 + measurements toward two nearby massive stars.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".